Innovative Applications of Artificial Intelligence in Electrified Railway Catenary Inspection: A Review of Computer Vision and Deep Learning Approaches

Abstract

The rapid advancement of artificial intelligence (AI) technologies has catalyzed transformative changes across numerous industrial sectors, with the railway industry standing as a particularly promising domain for implementation. This comprehensive review systematically examines the innovative applications of AI technology specifically within the critical field of electrified railway catenary inspection. As global railway networks expand and operational demands intensify, traditional inspection methodologies increasingly reveal limitations in efficiency, accuracy, and scalability. This paper analyzes AI's significant technical advantages, including capabilities for efficient large-scale data processing, precise pattern recognition, and adaptive learning from continuously generated operational data. We propose and elaborate multiple innovative application frameworks encompassing intelligent image recognition systems, data-driven predictive maintenance models, and intelligent decision-support architectures. These integrated frameworks demonstrate potential to substantially enhance inspection efficiency, improve fault detection accuracy, and elevate overall system intelligence levels within catenary monitoring ecosystems. Furthermore, this research provides substantive theoretical foundations and practical references to support the ongoing intelligent transformation of railway infrastructure management. The paper concludes with a forward-looking perspective on emerging trends and future development trajectories for AI technologies within the electrified railway sector, anticipating their expanding role in ensuring operational safety, optimizing resource allocation, and enhancing transportation efficiency across increasingly complex and demanding railway networks.